{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:6WINZCBJ67GMR62KTJOSR4CC5G","short_pith_number":"pith:6WINZCBJ","canonical_record":{"source":{"id":"2403.09508","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-14T15:55:53Z","cross_cats_sorted":[],"title_canon_sha256":"8aff9e74c31f554cbeb997ce4f18d580fe46b6a5d3e5d7917242e178675e626c","abstract_canon_sha256":"171a76a566454ce50480850ff9c13ed413b9fa09105443623c999b33aeb1e3cc"},"schema_version":"1.0"},"canonical_sha256":"f590dc8829f7ccc8fb4a9a5d28f042e98f70ddcbbe068185988247e1936a6870","source":{"kind":"arxiv","id":"2403.09508","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09508","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09508v3","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09508","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"pith_short_12","alias_value":"6WINZCBJ67GM","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"pith_short_16","alias_value":"6WINZCBJ67GMR62K","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"pith_short_8","alias_value":"6WINZCBJ","created_at":"2026-07-05T08:44:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:6WINZCBJ67GMR62KTJOSR4CC5G","target":"record","payload":{"canonical_record":{"source":{"id":"2403.09508","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-14T15:55:53Z","cross_cats_sorted":[],"title_canon_sha256":"8aff9e74c31f554cbeb997ce4f18d580fe46b6a5d3e5d7917242e178675e626c","abstract_canon_sha256":"171a76a566454ce50480850ff9c13ed413b9fa09105443623c999b33aeb1e3cc"},"schema_version":"1.0"},"canonical_sha256":"f590dc8829f7ccc8fb4a9a5d28f042e98f70ddcbbe068185988247e1936a6870","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:52.013573Z","signature_b64":"NUFBMZliVGuKs0z2SZxhgNGZtUpM3L4T+jH8yZa8/NnDh5l4abbv8GVIUZMiOloIABdf9SBE3gcxy91D8PppCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f590dc8829f7ccc8fb4a9a5d28f042e98f70ddcbbe068185988247e1936a6870","last_reissued_at":"2026-07-05T08:44:52.013141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:52.013141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.09508","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:44:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ixWoF7T6Am+ia5OLst6GidNoD1jIJud7WprG1fV92gCixjw/PPGALgLterRP04559RFCts9uMgBDtIakpX5BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:41:18.908041Z"},"content_sha256":"88bf518650f79f62d8e3cbc97c7e6c93618bcf7adebbacd2006e4c22e214388e","schema_version":"1.0","event_id":"sha256:88bf518650f79f62d8e3cbc97c7e6c93618bcf7adebbacd2006e4c22e214388e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:6WINZCBJ67GMR62KTJOSR4CC5G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jeonghyeok Do, Munchurl Kim","submitted_at":"2024-03-14T15:55:53Z","abstract_excerpt":"Skeleton-based action recognition, which classifies human actions based on the coordinates of joints and their connectivity within skeleton data, is widely utilized in various scenarios. While Graph Convolutional Networks (GCNs) have been proposed for skeleton data represented as graphs, they suffer from limited receptive fields constrained by joint connectivity. To address this limitation, recent advancements have introduced transformer-based methods. However, capturing correlations between all joints in all frames requires substantial memory resources. To alleviate this, we propose a novel a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09508","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.09508/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:44:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b9ui3Y3qEFrN7XOnrvjaUhPIlCQJyQIkals/Qv7Dqjgkg6hpyabmRPMZatmyMuG8/AAaaRIzQEGFnrN6rO8CAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:41:18.908557Z"},"content_sha256":"008dc362837b56a60194b6a5ecfd792c12aa3a5f962a9c7bc3745abdf1858b9b","schema_version":"1.0","event_id":"sha256:008dc362837b56a60194b6a5ecfd792c12aa3a5f962a9c7bc3745abdf1858b9b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6WINZCBJ67GMR62KTJOSR4CC5G/bundle.json","state_url":"https://pith.science/pith/6WINZCBJ67GMR62KTJOSR4CC5G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6WINZCBJ67GMR62KTJOSR4CC5G/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T12:41:18Z","links":{"resolver":"https://pith.science/pith/6WINZCBJ67GMR62KTJOSR4CC5G","bundle":"https://pith.science/pith/6WINZCBJ67GMR62KTJOSR4CC5G/bundle.json","state":"https://pith.science/pith/6WINZCBJ67GMR62KTJOSR4CC5G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6WINZCBJ67GMR62KTJOSR4CC5G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6WINZCBJ67GMR62KTJOSR4CC5G","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"171a76a566454ce50480850ff9c13ed413b9fa09105443623c999b33aeb1e3cc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-14T15:55:53Z","title_canon_sha256":"8aff9e74c31f554cbeb997ce4f18d580fe46b6a5d3e5d7917242e178675e626c"},"schema_version":"1.0","source":{"id":"2403.09508","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09508","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09508v3","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09508","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"pith_short_12","alias_value":"6WINZCBJ67GM","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"pith_short_16","alias_value":"6WINZCBJ67GMR62K","created_at":"2026-07-05T08:44:52Z"},{"alias_kind":"pith_short_8","alias_value":"6WINZCBJ","created_at":"2026-07-05T08:44:52Z"}],"graph_snapshots":[{"event_id":"sha256:008dc362837b56a60194b6a5ecfd792c12aa3a5f962a9c7bc3745abdf1858b9b","target":"graph","created_at":"2026-07-05T08:44:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.09508/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Skeleton-based action recognition, which classifies human actions based on the coordinates of joints and their connectivity within skeleton data, is widely utilized in various scenarios. While Graph Convolutional Networks (GCNs) have been proposed for skeleton data represented as graphs, they suffer from limited receptive fields constrained by joint connectivity. To address this limitation, recent advancements have introduced transformer-based methods. However, capturing correlations between all joints in all frames requires substantial memory resources. To alleviate this, we propose a novel a","authors_text":"Jeonghyeok Do, Munchurl Kim","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-14T15:55:53Z","title":"SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09508","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:88bf518650f79f62d8e3cbc97c7e6c93618bcf7adebbacd2006e4c22e214388e","target":"record","created_at":"2026-07-05T08:44:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"171a76a566454ce50480850ff9c13ed413b9fa09105443623c999b33aeb1e3cc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-14T15:55:53Z","title_canon_sha256":"8aff9e74c31f554cbeb997ce4f18d580fe46b6a5d3e5d7917242e178675e626c"},"schema_version":"1.0","source":{"id":"2403.09508","kind":"arxiv","version":3}},"canonical_sha256":"f590dc8829f7ccc8fb4a9a5d28f042e98f70ddcbbe068185988247e1936a6870","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f590dc8829f7ccc8fb4a9a5d28f042e98f70ddcbbe068185988247e1936a6870","first_computed_at":"2026-07-05T08:44:52.013141Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:44:52.013141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NUFBMZliVGuKs0z2SZxhgNGZtUpM3L4T+jH8yZa8/NnDh5l4abbv8GVIUZMiOloIABdf9SBE3gcxy91D8PppCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:44:52.013573Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.09508","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88bf518650f79f62d8e3cbc97c7e6c93618bcf7adebbacd2006e4c22e214388e","sha256:008dc362837b56a60194b6a5ecfd792c12aa3a5f962a9c7bc3745abdf1858b9b"],"state_sha256":"b0e2ed0fd862877258d703834f0e8cad31ddaa397de64a739d4ec0fa875a2d53"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jeQ0Co/Jn5LFdzazf3NgLkLGqs+8N169vaRJ9lVYTnogqA3YBrhP6SmR5E31cnp050Zs9f+3eBa6BzTKF2FTCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T12:41:18.912633Z","bundle_sha256":"73f35aac88c4811bda87785209fab81f38a6f21cf27a6b7ba9640a3651c7d530"}}